Innovative Method for Scene Depth Estimation from Traditional Oriental Landscape Paintings

Основні поняття

The author proposes a novel framework using CLIP-based image matching and two-step Image-to-Image translation to predict real scene images corresponding to oriental landscape paintings, addressing the challenges of depth estimation in such unique artworks.

Анотація

The study introduces a method to estimate the depth of traditional oriental landscape paintings, crucial for creating 3D sculptures for visually impaired individuals. By utilizing CLIP-based image matching and advanced translation models, the research aims to bridge the gap between these intricate artworks and modern technology. The approach involves converting painting images into pseudo-real scenes and then predicting high-quality real scene images for accurate depth estimation. Experimental results demonstrate the effectiveness of the proposed method in predicting realistic scene images corresponding to oriental landscape paintings.

Статистика

"Experimental results show that our approach performs well enough to predict real scene images corresponding to oriental landscape painting images."
"MiDaS [48] is one of the SOTA depth estimation models."
"Our method outperforms previous I2I translation methods in predicting real scene images."

Цитати

"Our research potentially assists visually impaired people in experiencing paintings in diverse ways."
"To achieve plausible real scene images corresponding to given oriental landscape painting images, CLIP-based matching, CycleGAN [63], and DiffuseIT [32] should be employed."

Ключові висновки, отримані з

Scene Depth Estimation from Traditional Oriental Landscape Paintings

by Sungho Kang,... о arxiv.org 03-07-2024

https://arxiv.org/pdf/2403.03408.pdf

Глибші Запити

How can preservation issues like splitting and blurring impact the outcomes of I2I translation?

Preservation issues like splitting and blurring in oriental landscape paintings can significantly impact the outcomes of Image-to-Image (I2I) translation. These issues can distort the structural integrity of the painting, leading to inaccuracies in the translated images. When an I2I model is trained on distorted or partially damaged images, it may struggle to accurately capture the original intent of the artwork. This can result in unrealistic translations and loss of important details that are crucial for maintaining fidelity between the original painting and its translated version.

What are the implications of removing text and stamps from oriental landscape paintings on depth estimation?

Removing text and stamps from oriental landscape paintings before conducting depth estimation tasks can have significant implications on the accuracy of depth maps generated. Text and stamps within a painting often provide contextual information about objects, distances, or spatial relationships depicted in the artwork. By removing these elements, essential cues that aid in understanding scene depth may be lost.
In terms of depth estimation specifically, text removal could lead to misinterpretations by pre-trained models as they rely on visual cues present in the image data. Without textual context, certain objects or features might be incorrectly interpreted by algorithms designed to estimate depths based on visual patterns alone. This could result in inaccurate depth maps that do not reflect true spatial relationships within the painting.

How can substantial amounts of data collection be facilitated despite restrictions on public release by museums?

Facilitating substantial data collection despite restrictions imposed by museums involves innovative strategies for accessing relevant datasets while respecting copyright laws and privacy concerns. Some approaches include:

Collaboration with Institutions: Establish partnerships with museums or cultural institutions to gain access to their collections for research purposes under specific agreements.

Data Sharing Agreements: Negotiate data sharing agreements with museums where researchers adhere to strict guidelines regarding usage rights, attribution requirements, and limitations on dissemination.

Digitization Efforts: Encourage digitization efforts within museums to create digital archives accessible for research while preserving physical artifacts.

Synthetic Data Generation: Develop synthetic datasets using generative models trained on publicly available artworks combined with domain-specific knowledge to simulate realistic representations without infringing copyright laws.

By employing these methods ethically and transparently, researchers can overcome challenges related to restricted access while promoting collaboration between academia and cultural heritage institutions for valuable data acquisition initiatives related to art analysis studies such as scene depth estimation from traditional Oriental landscape paintings."